Shared Control Data Transfer for Secure Automation Tuning

The patent extracts only the essential operational parameters and performance data from the uploaded information, separating them from sensitive proprietary information. The server processes and analyzes only the extracted operational data to generate optimization recommendations, while the original sensitive information remains local to each control device. This resolves the contradiction by enabling productivity improvement through data sharing while protecting security through selective extraction.

The server acts as an intermediary that receives uploaded information from local control devices, processes the data to extract operational parameters, generates optimization recommendations, and returns them to the control devices. The server never stores or exposes the original sensitive information, serving only as a processing mediator. This enables collaborative optimization across multiple devices while maintaining information security through the intermediary's controlled processing.

The system implements feedback loops where local control devices upload operational results and monitoring data to the server. The server analyzes this feedback from multiple sources, compares performance across different devices and configurations, and generates optimized recommendations based on collective learning. This feedback mechanism enables rapid adaptation by leveraging experiences from multiple devices simultaneously, dramatically reducing optimization time compared to independent trial-and-error approaches.

The server performs preliminary analysis of uploaded data from multiple control devices, pre-processing and comparing operational parameters before generating optimization recommendations. By preparing and analyzing data in advance from multiple sources, the system can provide ready-made optimized configurations that significantly reduce the time each individual device would otherwise need to spend on independent optimization trials.